Guided Real-Time Scanning of Indoor Objects
Young Min Kim, Niloy J. Mitra, Qixing Huang, Leonidas Guibas
Pacific Graphics 2013


Advances in 3D acquisition devices provide unprecedented opportunities for quickly scanning indoor environments. Such raw scans, however, are often noisy, incomplete, and significantly corrupted, making semantic scene understanding difficult, if not impossible. Unfortunately, in most existing workflows, scan quality is assessed after the scanning stage is completed, making it cumbersome to correct for significant missing data by additional scanning. In this work, we present a guided real-time scanning setup, wherein the incoming 3D data stream is continuously analyzed, and the data quality is automatically assessed. While the user is scanning an object, the proposed system discovers and highlights potential missing parts, thus guiding the operator (or an autonomous robot) as where to scan next. The proposed system assesses the quality and completeness of the 3D scan data by comparing to a large collection of commonly occurring indoor man-made objects using an efficient, robust, and effective scan descriptor. We have tested the system on a large number of simulated and real setups, and found the guided interface to be effective even in cluttered and complex indoor environments.



We are grateful to the anonymous reviewers for their feedback and Han Liu for some initial testing. The project was supported in parts by NSF grants CCF 1011228 and 1161480, ONR MURI award N00014-13-1-0341, a Google research award, the Max Planck Center for Visual Computing and Communications, a Marie Curie CIG, and ERC Starting grant SmartGeometry.


  title = {Guided Real-Time Scanning of Indoor Objects},
  author = {Young Min Kim and Niloy J. Mitra and Qixing Huang and Leonidas Guibas},
  journal = {Computer Graphics Forum (Proc. Pacific Graphics)},
  volume = {xx},
  issue = {x},
  pages = {xx},
  year = {2013}, 

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